The E ect of Education Policy on Crime: An Intergenerational

Transcrição

The Eect of Education Policy on Crime: An
Intergenerational Perspective∗
†
‡
§
Costas Meghir , Mårten Palme , Marieke Schnabel
May 2011
Abstract
The Swedish comprehensive school reform implied an extension of the
number of years of compulsory school from 7 or 8 to 9 for the entire nation
and was implemented as a social experiment by municipality between
1949 and 1962. A previous study (Meghir and Palme, 2005) has shown
that this reform signicantly increased the number of years of schooling
as well as labor earnings of the children who went through the post reform
school system, in particular for individuals originating from homes with
low educated fathers. This study estimates the impact of the reform on
criminal behavior: both within the generation directly aected by the
reform as well as their children. We use census data on all born in Sweden
between 1945 and 1955 and all their children merged with individual
register data on all convictions between 1981 and 2008.
We nd that
the educational reform decreased crime substantially for men who were
directly aected by it. We also nd that the crime rate declined for the
sons of those fathers directly aected by the new educational system;
we interpret this results as implying that improved education increased
resource and parenting quality, leading to improved child outcomes.
We thank Matthew Lindquist, Imran Rasul, Lisa Jönsson, Emilia Simeonova as well as participants at seminars at Stockholm University, University College London, Yale University and
the Institute for Labor Market Policy Evaluation (IFAU) in Uppsala for helpful comments and
suggestions. We gratefully acknowledge nancial support from IFAU. Costas Meghir thanks the
ESRC for funding under the Professorial Fellowship RES-051-27-0204 and under the ESRC Centre
at the IFS ESRC RES-544-28-5001.
†
Department of Economics, Yale University; Institute for Fiscal Studies and Department
of Economics, University College London, Gower Street, London WC1E 6BT, UK. E-mail:
[email protected]
‡
Department of Economics, Stockholm University, SE - 10691 Stockholm, Sweden. E-mail:
[email protected]
§
Department of Economics, University College London, Gower Street, London WC1E 6BT, UK.
E-mail: [email protected]
∗
1
1 Introduction
Crime imposes huge costs on society and policies to prevent crime are debated
around the world.
In addition to police resources and punishment of criminals,
these policies typically involve public spending on creating alternative labor outcomes for individuals potentially involved in criminal activities, e.g.
reforms of
national education systems. Since criminal behavior has an exceptionally strong intergenerational link, such policies may have an eect across generations. Although
there is a very large previous literature showing that low educated people are more
likely to be convicted and that their children are more likely to commit crimes, very
few studies have attempted to show a causal relation between own education and
criminal behavior and (to our knowledge) no previous study has shown a causal
relation between parental education and criminal behavior.
In this paper we study to what extent a major national education reform, which
increased schooling and earnings, also aected criminal behavior: Both in the generation directly aected by the reform as well as their children. The reform in question is the Swedish comprehensive school reform, which included an extension of
the number of years of compulsory schooling and was implemented gradually across
the 1,055 Swedish municipalities during the 1950s, before nally being rolled out
nationally in 1962. As described in Meghir and Palme (2005), the implementation
of the reform allows us to investigate its impact on crime by comparing individuals
living and working in the same labour market but having been exposed to dierent
schooling systems. Moreover, we can also estimate the impact of the reform on their
own children.
In this case we are able to consider how changing the educational
experience of the parents can aect important outcomes for their children. Beyond
the obvious public policy implications, the results speak to the growing literature
2
on the importance of parenting quality on child outcomes.
Two earlier papers by Lochner and Moretti (2004) and Machin, Marie, and Vuji¢ (2010) respectively study the relation between compulsory schooling laws and
criminal behavior. Lochner and Moretti (2004) use changes in compulsory schooling
laws across time between US states to identify the eect of increasing education on
crime. Machin, Marie, and Vuji¢ (2010) compare criminal behavior of the cohorts
just before and just after the implementation of the comprehensive school system
in Britain. Our paper goes further in two important ways. First, we compare the
criminal behavior of two groups, distinguished by the school system they were exposed to, but active in the same labor markets at the same point in time, and who
belong to the same cohort. As a result any general equilibrium eects of the reforms
are eliminated, as they are common across groups - what we identify is the impact
of being exposed to alternative educational systems, over and above the macroeconomic eects that result from this. However, the key distinguishing feature of our
paper is that we consider the eects on the criminal activity of the children of those
aected by the reform.
As we discuss later, the eects in this case will operate
through improved parenting and investments in children.
The paper is organized as follows: Section 2 discusses previous theoretical and
empirical work on the relation between both own education and criminal behavior
as well as parental education and criminal behavior; Section 3 provides an overview
of the comprehensive school reform in Sweden; Section 4 describes the data and
presents empirical results on the relation between educational attainments and criminal behavior as well as intergenerational associations of crime; Section 5 discusses
our identication strategy; Section 6 presents our empirical results on the eect of
the comprehensive school reform and the probability of being convicted for crime or
3
sentenced to prison; Section 7 concludes.
2 The Impact of Education on Crime
2.1 The Impact of Education on Crime within a Generation
The links between economic incentives and crime have been established both theoretically and empirically in earlier studies. A prominent example is Freeman (1999)
who outlines an economic model of crime where the choice between criminal and legal activity is determined by comparing the expected utility of each. Grogger (1998),
Gould, Weinberg, and Mustard (2002) and Machin and Meghir (2004) demonstrate
the importance of wages and labour market opportunities in driving crime. One implication of this is that improved education may reduce crime. A number of papers
have looked at the link between education and crime directly.
These include Lochner and Moretti (2004) and Machin, Marie, and Vuji¢ (2010),
cited above.
A more theoretically based approach was oered by Lochner (2004)
who develops a life cycle model of education and crime and estimates a negative
education-crime relationship. A study, based on this capital approach by Williams
and Sickles (2002) nds that years of schooling has a signicant negative eect
on crime in adulthood, and that there is a relationship between crime and other
measures of human capital. Earlier studies support this empirical evidence on the
education-crime relationship; for example Freeman (1996) states for the 1991 US
Census that two thirds of US prison inmates are high-school drop-outs and 12 percent of 24-35 year old high school drop outs were incarcerated in 1993. This negative
correlation between crime and education has also been documented in the the crim-
1
inology and sociology literature, for example Sabates and Feinstein (2008a).
1 See
also Sabates and Feinstein (2008b).
4
Gallipoli and Fella (2006) estimate a general equilibrium model of crime and
education. They nd that increases in education have a signicant impact in reducing crime. However, they point out that the general equilibrium eects, operating
through changes in wages as the number of educated individuals increases, can be
substantial.
2.2 The Eects of Parental Education on Children's Criminal
Behavior
Intergenerational associations of criminal behavior have been documented in the
criminology literature.
From the economics point of view this question relates to
the investments that parents make on their children and the way that parental
education may aect such investments (see Becker (1981)).
2
Of particular interest to our study are the two recent papers by Hjalmarsson and
Lindquist (2010a) and Hjalmarsson and Lindquist (2010b). The rst documents a
strong correlation between crime of fathers and children of both genders in Sweden
using the Stockholm Birth Cohort Study, which is a data set that consists of all
individuals born in 1953 and living in Stockholm in November 1963, containing
crime records of the birth cohort and their fathers.
The second study focuses on
parent-child correlations in crime using adoption data.
In particular they aim to
determine through which factors mothers and fathers inuence child criminality,
which is closely related to the study by Björklund, Lindahl, and Plug (2006).
2 For
some empirical work see for example Carneiro, Meghir, and Parey (2007). Moreover, there
is direct evidence that better childhood environments and early education can reduce crime rates,
see for example the results form the Perry pre-school experiment presented in Schweinhart, Montie,
Xiang, Barnett, Beleld, and Nores (2005) and Cunha and Heckman (2007).
5
2.3 Human Capital and participation in Crime - a simple
framework
To better understand the mechanisms through which educational reform can aect
participation in crime for both generations consider the following simple model.
Human capital is produced by investments in various stages of the child's life as
well as by overall educational attainment
investments
I0
Ec .
Suppose there are two stages, early
and investments during schooling
depends on the educational level of the parent,
I1 .
The eciency of investments
Ep .
Denote the human capital
production function by
H = H(I0 , I1 , Ec |Ep )
where
HI0 0 > 0, HI0 1 > 0, HE0 c > 0
and
HI000 < 0, HI001 < 0, HE00 c < 0.
Parents are
assumed to care about child quality, which here is just their human capital. Ignoring
dynamics for simplicity, they solve the problem
max {u(C, H)
C,I0 ,I1
where
C
st
C + I0 + I1 = Y p
is parental consumption.
will increase as parental resources
3
and
H = H(I0 , I1 , Ec |Ep )}
In this simple context investments in children
YP
increase, so long as
H
is a normal good. The
rst order conditions for investments are
∂H
= u0C
u0H ∂I
0
∂H
u0H ∂I
= u0C
1
An increase in the marginal productivity of such investments (say due to an increase
3 The
here.
problem is dynamic sequential, but nothing would be gained in introducing this notation
6
in parental education
Ep )
will lead to more investments in the children at both
stages. This will happen both because the productivity of investments may increase
and because parental resources
YP
go up. The next step is to see how these changes
can aect participation in crime.
Consider a very simple model of crime choice in two stages of life. First, is the
educational stage, where the individual can either engage in education, crime or
work. Then follows the
adult
stage where there is no education choice. We start by
the latter.
Committing a crime is a period by period decision with no dynamics (for simplicity). Working leads to income
Y (H) which depends on human capital H .
on the other hand yields a return
with probability
1 − p(H)
R
with some probability
leads to punishment
Moretti (2004), the dependence of
K
on
ing incarcerated. Moreover, it also costs
H
K(H).
p(H).
Being caught,
As discussed in Lochner and
represents the opportunity cost of be-
c(H)
to participate. This cost can reect
the aversion that one may have to anti-social behaviour. We assume that
increasing in
Crime
c(H)
is
H.
Participation in crime is determined by the condition
p(H)R − (1 − p(H))K(H) − c(H) − Y (H) > 0 ⇐⇒
An increase in
H
will increase earnings
implying a reduction in crime.
engage in crime
Y (H) and participation costs c(H), both
A possible mitigating eect is that better human
capital may make crime more eective and reduce the probability of capture
p(H).
In our empirical analysis we only measure convictions; we assume that a reduction
in convictions reects a reduction in crime participation and not more eective
7
criminals. Thus, other than the potential eect on
p(H),
increasing human capital
will decrease participation in crime.
It is also useful to consider the earlier period, when the individual still has the option of being in school. Dene the future value as
with
Vκ
denoting the value of crime and
Vw
V (H1 ) = E maxκ,w (V κ (H1 ), V w (H1 ))
the value for work.
the cost of education, which we assume are declining in
H0
ced (H0 )
denotes
(initial human capital).
In this rst period the value of education, crime and work respectively are given by
V ed = −ced (H0 ) + βV (H1+ )
V w = Y (H0 ) + βV (H0 )
V k = p(H0 )R − (1 − p(H0 ))K(H0 ) − c(H0 ) + βV (H0 )
where
H1+ denotes that education allows the individual to enter the next period with
higher human capital. The individual choose the activity with the greatest value.
First, note that if schooling is compulsory, then there is a mechanical reduction in
crime, simply because the opportunity to commit an oence is no longer there (or
reduced in practice). Second, an increase in human capital will increase the value
of both schooling and work; the former because it will reduce the costs of schooling
ced (H0 )
as well as the future value
V (H1+ ),
current wage as well as the future value
the latter because it will increase the
V (H0 ).
So rst period crime will decline;
whether education will go up is in this context ambiguous.
We can now summarise the way that educational reform and compulsory schooling in particular can aect crime across generations.
Meghir and Palme (2005)
document the impact of the reform on educational attainment and earnings.
8
For
understanding the impact of the reform they emphasise the importance of distinguishing individuals aected by the reform with parents with just the statutory level
of education (60% in that population) to those whose parents had attained a higher
level.
An increase in compulsory schooling reduces the available time for crime early on;
it increases human capital and thus reduces further the incentive to commit crimes
and may increase the chance of remaining in school beyond the new compulsory
level.
H.
It may also draw increased investments
I1
from parents further increasing
This reduces crime in the young (school period) ages. As an adult, the result is
increased human capital, which as explained before, will reduce
adult
crime. If there
is a habit formation aspect of crime (not included above), the early decline will be
reinforced. Thus crime will decline relative to the group that was not aected by
the reform.
The children of the aected generation are all experiencing the same education
system because the reform was rolled out nationally in 1962.
They dier by the
fact that some have parents who faced the new education system and as a result
have more parental education and more resources
to higher
I0 , I1 ,
Y p.
These dierences will lead
and eventually higher educational attainment
Ec
relative to the
children in the comparison group, whose parents did not go through the reform.
Educational attainment
Ec
may increase because, according to mounting evidence,
an increase in early investments
I0
improves cognition and social skills and hence
reduces the costs of education. In addition the increased resources
Yp
allow more
transfers to children alleviating somewhat nancial constraints for education. These
channels imply an increase in both
H0
and
described above.
9
H1
reducing crimes at all life stages, as
For individuals aected by the reform but having parents with more than statutory education the impact is less clear cut.
For this group there is no eect on
educational attainment. However it changed the way they were educated because it
abolished early selection and tracking, which aected primarily this group. It can
be argued that quality of education for this group was diluted for this reason
and
because the increase in compulsory schooling, aecting the other group, could have
reduced the quality of the peers.
For this reason we cannot be condent that
H
increased for this group. This is why in our empirical analysis we present overall
results as well as results separately on the lower socio-economic group.
3 The 1950 Education Reform and the Social Experiment
3.1 The Swedish school system before and after the reform
Prior to the implementation of the comprehensive school reform, pupils attended a
common basic compulsory school (
folkskolan ) until grade six.
After the sixth grade
pupils were selected to either continue one or, in mainly urban areas, two years in
the basic compulsory school, or to attend the three year junior secondary school
(
realskolan ).
The selection of pupils into the two dierent school tracks was based
on their past performance, measured by grades. The pre-reform compulsory school
was in most cases administered at the municipality level.
The junior secondary
school was a prerequisite for the subsequent upper secondary school, which was
itself required for higher education.
In 1948 a parliamentary school committee proposed a school reform that im-
10
4
plemented a new nine-year compulsory comprehensive school.
The comprehensive
school reform had three main elements:
1. An extension of the number of years of compulsory schooling to 9 years in the
entire country.
2. Abolition of early selection. Although pupils in the comprehensive schools were
able to choose between three tracks after the sixth grade - one track including
vocational training, a general track, and an academic level preparing for later
upper secondary school - they were kept in common schools and classes until
the ninth grade.
3. Introduction of a national curriculum.
The pre-reform compulsory schools
were administrated by municipalities and the pre-reform curriculum varied
between municipalities.
3.2 The Social Experiment
The social experiment with the new comprehensive nine-year compulsory school
started during an assessment period between 1949 and 1962, when the nal cur-
5
riculum was decided.
The proposed new school system, as described above, was
introduced in municipalities or parts of city communities, which in 1952 numbered
1,055 (including 18 city communities). The selection of municipalities was not random. However, the decision to select the areas was based on an attempt to choose
areas that were representative for the entire country, both in terms of demographics
as well as geographically. At rst the National Board of Education contacted the
4 The
school reform and its development are described in Meghir and Palme (2003), Meghir and
Palme (2005), and Holmlund (2007). For more detailed reference on the reform, see Marklund
(1980) and Marklund (1981).
5 The ocial evaluation National School Board (1959) was mainly of administrative nature.
Details on this evaluation are also described in Marklund (1981).
11
municipalities, or sometimes they themselves applied to participate. From this pool
of applicants a "representative" sample of municipalities was chosen. Table 12 in
the Appendix shows the take up rates of the reform between 1949 and 1962.
In
our analysis we consider cohorts born between 1945 and 1955. Figure 1 shows the
number of observations in each one-year birth cohort and the proportion assigned
to the reform.
Figure 1: Proportion of individuals in sample assigned to the reform
4 The Data
4.1 Data Sources and Descriptive Statistics
We use a sample originally obtained from Sweden's population census.
To link
individuals across generations we used the multi-generation register, provided by
6
Statistics Sweden.
We are able to link and use three generations in our analysis:
6 Statistics
Sweden(2003) Flergenerationregistret 2002. En beskrivning av innehåll och kvalitet.
Statistics Sweden. Avdelning för Befolknings och Välfärdsstatistik.
12
the
parent generation
which is the generation directly aected by the reform and
it consists of all individuals born in Sweden between 1945 and 1955, their parents
the
grandparent generation,
the
parent generation.7
655,801 females in the
the
children generation
which are the children of
This corresponds to 1,340,857 persons, 658,056 males and
parent generation.
date and parish of birth.
generation,
and the
From the birth certicates we know gender,
We restrict our sample of the children of the
children generation,
parent
to those who have reached the age of criminal
responsibility (age 15) in 2008, the last year for which we have crime records. This
corresponds to 1,621,758 children, 833,564 sons and 788,194 daughters in the
generation
children
that were born between 1959 and 1991.
The reform assignment variable is obtained in two steps. First, we use the name
of the church parish of birth in order to obtain the municipality code according to the
1953 Swedish municipality division. Second, based on the year and municipality of
birth, we use an algorithm based on historical evidence on reform implementation in
each municipality provided by Helena Holmlund and described in Holmlund (2007)
to assign reform status to each individual in the sample.
Information on the individual's highest education level was obtained and matched
on to our sample from the Swedish National Education Register. For the
ent generation
grandpar-
we used data from the 1970 census, which only provides information
on individuals younger than age 60 in the year of the census, allowing us to obtain
education information for 78.4 percent of mothers and for 65.8 percent of fathers
of the
parent generation.
We analyze the eects of the reform separately for those
individuals originating from the
ned as those individuals of the
low educated grandparent generation.
grandparent generation
7 Even
This is de-
with the lowest pre-reform
though we have information on biological and adoptive parents and children, we exclude
all individuals who have been adopted, or who have adopted children themselves.
13
statutory level of compulsory schooling. Hence, we analyze the eects of the reform
separately for the
parent generation
with low educated fathers, and for the
children
generation with low educated grandfathers, which amounts to roughly 63-65 percent
8
of the sample with available education information.
Information on all convictions in entire Sweden covering the time period between
1981 and 2008 is provided by the Swedish National Council for Crime Prevention
(Brå) and has been linked to individuals in our data set. One conviction/court trial
often covers several crimes. We have detailed information on the number of crimes
the person has been convicted for in each trial, the date of conviction, as well as the
penalty for each crime.
Table 1: Number of all convictions in Sweden 1981-2008, for cohorts
born 1945-1955 and their children
Ever convicted
Ever convicted to prison
Male
Female
Male
Female
Number
173,395
46,633
36,870
3,126
Percent
25.31
7.11
5.38
0.48
Number
68,860
16,673
14,525
1,255
Percent
23.96
6.11
5.05
0.46
Number
220,494
69,843
28,588
2,001
Percent
26.45
8.86
3.43
0.25
Parent generation
Low educated fathers
Children generation
Low educated paternal grandfathers
Number
64,976
20,183
7,361
482
Percent
24.68
8.12
2.80
0.19
Low educated maternal grandfathers
Number
76,887
22,621
9,554
625
Percent
27.78
8.65
3.45
0.24
Notes: We only report the number of convicted children for low educated
grandfathers, on both sides of the family.
8 Table
13 in the data appendix summarizes the number of available observations in each generation and subgroup.
14
Table 1 shows the number of convicted persons for the two generations, the 19451955 cohorts and their children, covered by our data on convictions. Over this time
window, 25 percent of all males in the
parent generation have been convicted at least
once, and over 5 percent have been to prison. Only 7 percent of women have been
convicted, and 0.5 percent have received a prison sentence. Importantly, the data on
criminal convictions only cover the time range between 1981 to 2008, which means
that the generations born between 1945 and 1955 will be between the ages of 26 to
63, whereas their children's convictions cover the ages of 15 to 49. The picture for
the children generation looks very similar to the one of their parents, with slightly
higher percentages of the population having been convicted, possibly attributable
to the younger age window. Conviction rates for those with low educated fathers
or grandfathers are presented in the lower panel of Table 1. This shows, perhaps
surprisingly, that the conviction rates are only marginally higher for this group than
for the whole population.
Table 14 in the data appendix shows the crime-age distribution for the entire
data set not only covering the cohorts of interest. The largest amount of convictions
are for people between 15 and 24, followed by the age range 25 to 34, and further
decreasing with age.
This pattern of convictions by age is also shown in gures
1 and 2 in the appendix that show the average rate of convictions by age and by
cohorts for the cohorts 1970-1989 using men in our children sample.
The stated conviction rates for men of roughly 25 percent is a surprisingly high
proportion of the population, which prompted us to look into this in greater detail.
First, note that the type of crimes included in our data have to be severe enough
to involve a trial and a conviction in court. This includes the more serious trac
violations such as driving without a licence, driving under the inuence of alcohol or
15
drugs, and causing bodily harm, but does not include speeding or parking tickets.
As such they do represent serious anti-social behavior. Unfortunately, we were not
yet given the specic type of crime for which an individual in our data has been
convicted for.
9
However, a good idea of the composition of crime can be obtained
in Table 16 in the data appendix where we show a breakdown of type of crime
convictions in 2009.
In addition to the data on convictions we have data on all suspected crimes between 1991 and 2009. It includes a variable that gives a detailed code on the type
10
of suspected crime.
Although this data overstates actual charges and crimes we
use it to provide an idea of the distribution of trac crimes. Table 17 presents all
categories that are related to trac violations and the number of oences between
January 1991 and June 2009. The total number of suspected crimes during this time
were 4,073,985 of which 16.9 percent were trac crimes. Again, all of these trac
crime categories are severe violations. Additional support of such high conviction
rates in Sweden is provided by previous studies that have shown similar conviction rates, see Hjalmarsson and Lindquist (2010a) and Hjalmarsson and Lindquist
(2010b).
4.2 The Association of Education, Family and Criminal Behavior
Table 2 shows the Linear Probability Model estimation results of whether an individual has ever been convicted (column (1)) and ever been convicted to a prison
sentence (column (2)) on years of own schooling as well as on years of father's and
9 We
are waiting to obtain a variable that indicates the type of crime from the Brå crime registry.
coding of crime types in: Kodning av brott, Anvisningar och regler, Version 8.0,
Reviderad 1. Juli 2010, brå brottsförebyggande rådet.
10 Detailed
16
mother's schooling for the children generation. The estimations for the sample of
men born in Sweden between 1945 and 1955 are presented in the upper panel A,
and the ones for the sons in the lower panel B. All regression models include both a
full set of dummy variables for the birth cohorts and birth municipalities to control
11
for area and cohort xed eects.
Column (1) in panel A shows that one year of
own schooling for men in the parent generation is associated with a decrease of the
probability of a conviction by 1.9 percentage points, which translates to a reduction
in the conviction rate of 7.5 percent. One year of schooling for men decreases the
probability of a prison sentence by 0.8 percentage points, or a reduction in the total
share of men convicted to prison by 15 percent, see column (2). Panel B includes
own schooling and both parents' education for sons, and hence estimates the eect of
an increase of schooling of one parent controlling for assortative mating eects. One
year of own schooling is associated with a 2.64 (0.59) percentage points decrease in
the probability of sons' conviction (imprisonment), corresponding to a 9.96 (17.06)
percent reduction in the share of convicted (imprisoned) sons. A years decrease in
father's schooling is associated with an increase in the son's conviction rate (prison
rate) of 0.568 (0.118) percentage points or equivalently a 2.15 (3.44) percent reduction in the share of convicted (imprisoned) sons. The equivalent gure for a decrease
in mother's education by a year is a 2.261 (4.198) percent decrease in the share of
convicted (imprisoned) sons.
We present the relationship between crime and the levels of education in the data
appendix (see Tables 18 and 19), revealing a steep decline in crime participation
associated with higher levels of own and parental education.
A similar decline is
also recorded for incarceration rates.
11 When
computing the standard errors we cluster by birth municipality, which is conservative
and makes the standard errors comparable to the ones we estimate for the eect of the reform
below.
17
Table 3 shows the results of a Probit model of the association between having
a father who has been convicted (or to prison) and the father's age of birth on the
probability of his son ever been convicted (or to prison).
The probability of ever
been convicted increases by over 15 percentage points if a son has a convicted father.
This corresponds to a 63 percent increase of the total share of convicted sons. If
he had a father who had been convicted to a prison sentence the probability of an
own prison sentence increases by over 7 percentage points, which translates to a 134
percent increase in the share of convicted sons.
Overall, there is a large and signicant negative correlation between own education and own criminal participation, both for the probability of a conviction and
the probability of a conviction to prison. This holds for two subsequent generations.
Furthermore, our evidence strongly supports the notion that higher education of
both mothers and fathers is associated with a lower probability that their sons will
be engaged in crime. Finally, we nd a strong and highly signicant positive intergenerational link between father's and son's criminal behavior.
18
Table 2:
Linear probability model estimates of the association between own or
father's education and the probability of being convicted and the probability of
being convicted to a prison sentence. Men born between 1945-1955 and their sons.
Panel A
Dependent variables
Men born 45-55
Probability conviction
Probability prison
p̄ = 0.2531
p̄ = 0.0538
-1.916***
-0.806***
(0.093)
(0.057)
-7.570
-14.981
y
y
662,875
662,875
Years of schooling, own
Corresponding percentage change
Birth cohort/municipality dummies
Observations
Panel B
Dependent variables
Sons of men born 45-55
Probability prison
p̄ = 0.2645
p̄ = 0.0343
-2.635***
-0.585***
Years of schooling, own
Years of schooling, father
Years of schooling, mother
Birth cohort/municipality dummies
Observations
(0.029)
(0.013)
-9.962
-17.055
-0.568***
-0.118***
(0.035)
(0.014)
-2.147
-3.440
-0.598***
-0.144***
(0.026)
(0.009)
-2.261
-4.198
y
y
675,625
675,625
Notes: Results are scaled by 100. Level of observation in upper panel are men born in Sweden
between 1945 and 1955. Level of observation in lower panel are sons of parents born between
1945 and 1955. Robust standard errors in parentheses, clustered by birth municipality. All
regressions in the upper panel include a full set of birth cohort dummies and birth municipality
dummies. In the lower panel a full set of municipality dummies and birth cohort dummies of
the child was included. Signicance levels *** p<0.01, ** p<0.05, * p<0.1.
19
Table 3: Probit model marginal eects of the association between the probability of
ever been convicted and the father having ever been convicted. Marginal eects are
scaled by 100. Estimations for dierent education levels of grandfathers. Sons of men
born in Sweden between 1945 and 1955.
Panel A
Dependent variables
Father convicted/Father prison
Percent change
Age of father at birth of son
Observations
Probability prison
p̄ = 0.245
p̄ = 0.054
15.457***
7.248***
(0.260)
(0.183 )
63.090
134.222
-1.759***
-0.323 ***
(0.019)
(0.007)
559,085
559,085
Panel B
Sons of men born 45-55 with low educated father
Dependent variables
Probability prison
p̄ = 0.245
p̄ = 0.021
14.755***
7.116***
(0.245)
(0.243)
60.224
338.857
-1.765***
-0.315***
Father convicted/Father prison
Percent change
Age of father at birth of son
Observations
(0.022)
(0.007)
241,716
241,716
Notes: Level of observation in upper panel are sons of men born in Sweden between 1945 and 1955,
and in the lower panel sons of men born between 1945 and 1955 who have a low educated father.
Robust standard errors in parentheses, clustered by birth municipality of sons. All regressions
include a full set of birth cohort dummies of fathers. Signicance levels *** p<0.01, ** p<0.05,
* p<0.1.
20
5 Identication Strategy
The main outcome variable we use is whether an individual was ever convicted
during the observation window 1981-2008. We also consider whether someone has
ever received a prison sentence. Finally, we consider the number of convictions.
All the analysis is done for males only and we distinguish them by the education
of the
grandparent generation.
We present two sets of estimates.
The rst relate
to the impact of the reform on the parent generation, i.e. the generation aected
by the educational reform directly. The second relate to the impact of the reform
on the children of the parent generation. Referring back to the discussion on how
the reform aects crime, for the parent generation the estimated impact related to
adult crime only because the youngest person in the sample is 26 when the crime
records start. Hence, the eect is not attributable to the simply keeping the kids o
the streets by getting them to attend school. For the child generation the impact
is attributable to better parenting and increased investments in children; it is not
due to dierences in the schooling system they are facing, which is the same for the
treatment and comparison group children.
If the reform had been randomised we could just compare crime rates across
treatment and control municipalities at each point in time. However such randomisation did not take place and as a result we need further identifying assumptions so
as to account for potential dierences across treatment and control municipalities.
Assuming that the propensity to commit a crime can be written as
∗
= F (, t),
yi,m,t
where t denotes cohort and
denotes unobserved heterogeneity. As in Athey and
21
Imbens (2006) and Altonji and Blank (1999) the key assumptions are that (i) the
distribution of
,
while diering across municipalities, does not change from cohort
to cohort and (ii)
F
is strictly monotonic in
.
These "dierence-in-dierences"
assumptions are sucient to identify the impact of the reform, but do imply restrictions on the model for crime participation equation 2.3. In particular it requires that
if there is any unobserved heterogeneity, whose distribution depends on the municipality, this has to be summarised by a single unobserved factor, say entering through
human capital, and whose distribution is the same across cohorts; the propensity
to commit a crime must be
strictly
monotonically related to this unobserved factor.
In practice this assumes that increases in human capital always reduce crime and
that this is never reversed by the reduction in the probability of being caught, which
does not seem to be a very strong assumption.
With a discrete outcome variable, such as ours we need additional assumptions if
12
we are to obtain a point estimate.
We thus assume that heterogeneity and cohort
eects are additively separable and we use both the linear probability model and
the logit model as alternative distributional assumptions. Specically we assume
∗
yi,m,t
= α + β1 Ri,m,t + γ10 ti + γ20 Mi + i,m,t ,
where
∗
yi,m,t
is the latent crime "intensity" outcome observed for person
municipality m and in birth cohort
t.
A conviction corresponds to
is the reform indicator, which equals one if individual
i
i
∗
yi,m,t
> 0. Ri,m,t
ti
is a vector of
belongs to and
vector of dummy variables indicating in which municipality individual
12 This
born in
belongs to a municipal-
ity and cohort that has been assigned to the new school system;
dummy variables indicating to which cohort individual
i
i
Mi
is a
was born.
is the case with all papers that employ dierence-in-dierences analysis of discrete outcomes (see Athey and Imbens (2006)).
22
Identication follows by assuming that
E(i,m,t |Ri,m,t , ti , Mi ) = 0.
The key assumptions are that the distribution of the unobservables are invariant
over time and that the crime trends in the treated groups are the same as those in
the comparison groups. The long time series of the data where we observe criminal
activity later in life of cohorts who were not aected by the reform but went to
school in municipalities that later implemented the reform and indeed also for several
cohorts that went to the new school system allows us to present some corroborative
evidence in support of the common trends assumption.
Our rst approach is the linear probability model where
∗
Pr(yi,m,t
> 0|Ri,m,t , ti , Mi ) = α + β1 Ri,m,t + γ1 ti + γ2 Mi .
This is straightforward to estimate by GLS. However, the linear probability model
has the well known disadvantage that it does not restrict the probabilities to lie on
the [0,1] interval. The main reason we use it is for computational convenience, given
the fact that there are about 1,000 municipality and 11 cohort xed eects.
As an alternative, we also estimate a logit model by minimum distance: rst we
group the data by municipality and cohort and estimate the within-cell conviction
probability (Pmt ).
We then use minimum distance to impose the restriction that
this probability is generated from a logistic distribution with a linear index as in the
latent equation above by tting the log-odds ratio as follows
23
log(
Pmt
) = δ0 + δ1 Rm,t + δ20 ti + δ40 Mi .
1 − Pmt
We drop all cells where the log odds ratio is not dened, which amounts to
about 6% of cells. Implicitly the logit and the LP models deal with such cells as
well as with the nonlinear form of the probabilities in a dierent way and hence it
is interesting to see if we get dierent results with the two approaches.
6 Results
6.1 The First Generation
Tables 4 through 7 show the estimates of the causal eect on criminal behavior of
being assigned to the reform. Tables 4 and 5 show the results for the probability of
having ever been convicted for the linear probability and logit model, respectively.
Table 6 shows the results for the probability of having ever been convicted to a
prison sentence using the linear probability model and, nally, Table 7 shows the
eect of the reform on the intensity to engage in crime measures by the number of
crimes an individual was convicted for. Throughout, we present results for the entire
sample of men as well as separately for those coming from homes with low educated
fathers. All specications include xed eects for birth municipality as well as birth
cohorts and the standard errors are corrected for clustering within municipality of
13
birth, allowing for both spatial and serial correlation.
To avid the computational diculties involved in estimating a logit model with
1,000 municipality xed eects and 11 cohorts we use a minimum distance procedure.
13 On
clustering the standard errors see Moulton (1990).
24
We collapse the sample to 10,744 municipality-cohort cells by computing the logodds ratio within each cell.
For 691 municipality-cohort cells the proportion of
observed crime was zero and hence the log-odds ratio is not dened. For 108 cells
we cannot assign the reform status, which leaves us with 9,949 municipality-cohort
observations.
We then regress the log-odds ratio on the municipality and cohort
dummies as well as on the reform indicator using GLS. Each cell was weighted by
p
pc (1 − pc )Nc ,
where
Nc
is the cell size and
pc
is the within cell probability of
a conviction. The corresponding marginal eects for dierent cohorts of the logit
model are presented in Table 5. For the estimates when prison sentences are used
as dependent variable this is not possible since the proportion of prison sentences is
too small, which prevents us from computing the log-odds ratios.
Column (1) in Tables 4 and 5shows a negative, but insignicant eect of the
reform on the probability of having ever been convicted for the entire sample born
between 1945 and 1955 and, in the lower panel of the tables, a similar result for
those originating from homes with low educated fathers.
However, these results
may be diluted by the fact that the oldest birth cohorts in our data are quite old
when we start to collect data on convictions and therefore may have ended their
criminal careers. For example, the oldest cohort included, those born in 1945, are
aged 36 when we start to record their criminal behavior. This is an age by which a
large proportion have already stopped committing crimes.
To account for this possibility, we also present results where we have restricted
the sample to younger birth cohorts. Columns (2) through (6) in Tables 4 and 5
show results for the impact of the reform on dierent cohort groups.
Although,
we clearly loose some precision as we reduce the number of cohorts considered, the
results show a clear pattern for both the entire sample and those originating from
25
homes with low educated fathers: for younger cohorts, which are observed at the
age range when more crimes are committed, the reform seems to have increasingly
strong eects. The estimate for the youngest cohort, born in 1954 or 1955, is highly
signicant suggesting a 1.3 percentage points decrease in crime; this corresponds to
a 5 percent, decrease in the probability of ever been convicted as a result of being
assigned to the post reform school system. The eect is somewhat larger in the subsample of men born in homes with low educated fathers. These results are consistent
with ndings by Lochner and Moretti (2004) and Machin, Marie, and Vuji¢ (2010)
for the US and the UK respectively, who show that increases in compulsory schooling
led to reductions in crime.
To analyze if the education reform also decreased the amount of crimes individuals were convicted for we use an alternative outcome variable that measures the
total number of crimes an individual has been convicted for between 1981 and 2008.
The average number of convicted crimes for men in the rst generation ranges from
1.2 to 1.8, depending on cohort and father's education level. This outcome variable
is characterized by a large mass point at zero and overdispersion with a variance
roughly ten times larger than the mean.
To estimate the model on this outcome
variable we use a negative binomial model.
Table 7 presents the marginal eects
from these estimates. The estimates are negative and signicant at the 10 percent
level for those born between 1950 and 1955 in homes with low educated fathers.
The eect seems to be somewhat stronger for those born between 1952-1955 and
1953-1955, respectively. These results are in line with the ndings on the probability
of having ever been convicted.
26
Table 4: Linear Probability Model estimates of the eects of the reform on the probability
of ever been convicted. Marginal eects are scaled by 100. Estimations by birth cohorts
for the entire sample and for men with low educated fathers separately.
Men born in
Sweden between 1945 and 1955.
Panel A
Cohorts
Men born 45-55
45-55
50-55
51-55
52-55
53-55
54-55
0.2531
0.2683
0.2714
0.2746
0.2783
0.2837
Reform
-0.645
-0.456
-0.532*
-1.028***
-1.076**
-1.329***
(0.405)
(0.305)
(0.318)
(0.396)
(0.490)
(0.479)
-2.548
-1.700
-1.960
-3.744
-3.866
-4.685
622,583
319,093
263,592
210,399
157,155
103,761
Percentage change
Share convicted
Observations
Panel B
Cohorts
Men born 45-55 with low educated father
45-55
50-55
51-55
52-55
53-55
54-55
0.2396
0.2517
0.2552
0.2588
0.2631
0.2693
Reform
-0.263
-0.494
-0.579
-1.119*
-1.103
-2.094**
(0.304)
(0.434)
(0.486)
(0.661)
(0.912)
(0.970)
-1.098
-1.963
-2.269
-4.324
-4.192
-7.776
264,679
150,620
125,952
101,266
76,207
50,222
Percentage change
Share convicted
Observations
Notes: The table presents results
p from a linear probability (LP) estimation with weighted least
squares. The used weights are: x0 b(1 − x0 b), which are obtained from a rst stage OLS estimation.
The level of observation are men born between between 1945-1955, or men born in cohorts indicated
at the top of each column; Reform is a dummy variable indicating the reform status of the index
person. All regressions include a full set of birth municipality dummies and birth cohort dummies.
Robust Standard errors in parentheses, clustered by municipality of birth. *** p<0.01, ** p<0.05,
* p<0.1.
6.2 The Child Generation
Tables 8 and 9 report the results of the dierence-in-dierences estimation of the
eects of the school reform on criminal participation of the child generation for the
probability of ever been convicted and prison convictions, respectively. Again, for
the probability of being convicted, we additionally estimate a logit model based
on cohort-municipality cells.
We estimate two specications.
In the rst one, we
estimate the eects of a father who has been subjected to the new school system on
27
Table 5: Logit model marginal eects of the reform on the probability of ever been
convicted. Marginal eects are scaled by 100. Estimations by birth cohorts for the
entire sample and for men with low educated fathers separately. Men born in Sweden
between 1945 and 1955.
Panel A
Men born 45-55
Cohorts
45-55
50-55
51-55
52-55
53-55
54-55
0.253
0.268
0.271
0.275
0.278
0.284
Reform
-0.023
-0.284
-0.434
-1.274*
-0.999
-1.395
(0.281)
(0.422)
(0.500)
(0.654)
(0.911)
(1.361)
-0.091
-1.057
-1.598
-4.639
-3.591
-4.916
622,583
319,093
263,592
210,399
157,155
103,761
Percentage change
Share convicted
Observations
Panel B
Men born 45-55 with low educated father
Cohorts
45-55
50-55
51-55
52-55
53-55
54-55
0.234
0.252
0.255
0.259
0.263
0.269
Reform
-0.022
-0.272
-0.417
-1.227*
-0.965
-1.351
(0.271)
(0.405)
(0.480)
(0.630)
(0.879)
(1.318)
-0.093
-1.081
-1.633
-4.741
-3.666
-5.015
264,679
150,620
125,952
101,266
76,207
50,222
Percentage change
Share convicted
Observations
Notes: We only report marginal eects of the logit model; Reform is a dummy variable indicating the reform status of the index person. All regressions include a full set of birth municipality
dummies and birth cohort dummies. The level of observation are men born between 1945-1955,
or men born in cohorts indicated at the top of each column. Robust Standard errors in parentheses, clustered by municipality of birth of the individual. *** p<0.01, ** p<0.05, * p<0.1.
child criminal behavior and in the second one the corresponding eects of a mother
going through the post reform school system.
We distinguish between four samples. The rst is the sample of individuals whose
father was born between 1945 and 1955 for which we estimate the eect of their
father's school reform assignment on their probability of a conviction. The second
sample are individuals whose mother was born between 1945 and 1955, for which we
estimate the eect of their mother's school reform assignment on their son's criminal
behavior. The other two samples are individuals originating from the
28
low educated
Table 6: Linear Probability Model estimates of the eects of the reform on the probability of
ever been convicted to a prison sentence. Marginal eects are scaled by 100. Estimations by
birth cohorts for the entire sample and for men with low educated fathers separately. Men
born in Sweden between 1945 and 1955.
Sample
Probability prison
Reform
Corresponding percentage
Men born 45-55
Men born 45-55 with low educated fathers
p̄ = 0.0538
p̄ = 0.0505
-0.149
-0.049
(0.160)
(0.170)
-2.770
-0.974
622,583
264,679
change share prison
Observations
Notes: The table presents
p results from a linear probability (LP) estimation with weighted least squares.
The used weights are: x0 b(1 − x0 b), which are obtained from a rst stage OLS estimation. The level
of observation are men born between 1945-1955. 5.38% of all men in the considered sample have been
convicted to prison at least once. 5.05% of the considered sample who have a low educated father have
been to prison. Robust Standard errors in parentheses, clustered by municipality of birth. *** p<0.01,
** p<0.05, * p<0.1. Reform is a dummy indicating the reform status of the index person. All regressions
include a full set of birth municipality dummies and birth cohort dummies.
grandparent generation.
More specically, the third sample is dened like the rst
one, but restricted to those individuals whose grandfather on the father's side is low
educated.
Finally, the fourth sample is restricted to those individuals within the
second sample whose grandfathers on their mother's side are low educated.
When we collapse the data set by cohort-municipality level as a rst step to
estimating the logit model, we do it by father's cohort-municipality level which gives
us 10,607 cells for the father's sample, and 10,247 for the sample with low educated
paternal grandfathers.
For the specication with mother's reform assignment we
collapse the data by mother's cohort-municipality level which leads to 10,647 for the
entire sample and 10,324 for the low educated maternal grandfathers sample.
Table 8 reports the marginal eects of the linear probability model for a conviction (denoted by LP), the log-odds ratio estimate for the logit model, and the
corresponding marginal eect of the logit model, both for the sample of all sons,
and for sons whose grandfather was low educated. It can be seen that the reform
29
Table 7: Marginal eects of negative binomial estimation of the eects of
the reform on the number of crimes men have been convicted for between
1981-2008. Estimations by birth cohorts for the entire sample and for men
with low educated fathers separately.
Men born in Sweden between 1945
and 1955.
Sample
Men born 45-55
Cohorts
45-55
50-55
51-55
52-55
53-55
54-55
ȳ
1.309
1.578
1.646
1.696
1.748
1.828
Reform
0.027
-0.071
-0.038
-0.122
-0.078
-0.121
(0.035)
(0.062)
(0.067)
(0.086)
(0.083)
(0.092)
622,583
319,093
263,592
210,399
157,155
103,761
Observations
Sample
Cohorts
45-55
50-55
51-55
52-55
53-55
54-55
ȳ
1.253
1.473
1.539
1.587
1.647
1.717
Reform
-0.030
-0.137*
-0.117
-0.250**
-0.236*
-0.177
(0.047)
(0.081)
(0.096)
(0.124)
(0.128)
(0.168)
264,679
150,620
125,952
101,266
76,207
50,222
Observations
Notes: Margnial eects of the negative binomial estimation are reported. Reform is
a dummy indicating the reform status of the individual. Robust Standard errors in
parentheses, clustered by individual's municipality of birth. *** p<0.01, ** p<0.05,
* p<0.1. All regressions include a full set of birth municipality dummies and birth
cohort dummies of the index person. The estimation method is a negative binomial model to relax the Poisson model assumption of equal conditional mean and
conditional variance of the outcome variable, but maintaining an exponential mean.
signicantly reduces the probability of having ever been convicted for the sons of
fathers who went through the reform by 0.6 percentage points, see column (LP).
Since the average share of convicted individuals in this cohort was about 26.5 percent, reduction in criminality was approximately 2.5 percent.
The log-odds-ratio estimates from the logit model translate into a marginal effect of a 0.646 percentage points decrease in the probability of a conviction, which
is very similar to the marginal eect obtained from the linear probability model.
Hence, the logit model suggests similar to the linear probability model, that father's
reform assignment signicantly reduces the total share of convicted men by about
2.5 percent.
30
Consider now the sample of sons with a low educated paternal grandfather. The
impact of their father being assigned to the reform is a reduction of the probability
of conviction by 1.02 percentage points. This translates into a 4.13 percent decrease
in the share of convicted sons of low educated grandfathers (24.7 percent).
The
marginal eect of the logit model is of the same magnitude (-0.97 percentage points,
and -3.94 percent decrease in total share of convicted).
There are no signicant eects of reform assignment of mothers on the probability
of a conviction of their sons neither for the entire sample of sons, nor for those with
a low educated maternal grandfather. Also, as can be seen in Table 9, the results of
the linear probability model for the eects of the reform status of father and mother
on the probability of a prison sentence of sons show no signicant eects.
Table 10 shows the negative binomial estimates of the marginal eects for the
intensive margin on the number of crimes convicted for between 1981 and 2008 for
the children generation. The top panel A shows the results based on father's reform
status for all children and those with low educated grandfathers, respectively. The
bottom panel B shows the corresponding results based on mother's reform status.
The estimate based on father's reform status shows a signicant negative eect of
the reform based on father's reform status, which is in line with our ndings on the
probability of being convicted.
We repeated the analysis for the children generation using the suspected crime
data mentioned above. Descriptive tables on suspected crime rates for the relevant
sample of children is presented in the appendix in table 15. Interestingly, the rates
of ever been suspected for a crime including and excluding all trac crimes are very
similar. We nd a negative but not signicant relationship between father's reform
assignment and sons probability of ever been suspected for a crime.
14 The
results are provided by the authors upon request.
31
14
32
595,138
595,138
(0.014)
(0.249)
-0.600
0.012
Logit
595,138
0.851
(0.277)
0.225
Logit Marginal eect
255,075
-0.147
(0.331)
-0.041
LP
255,075
(0.018)
0.021
Logit
255,075
1.509
(0.363)
0.419
Logit Marginal eect
p̄ = 0.278
243,082
-3.938
(0.366)
-0.972***
Sons of women born 45-55 with low educated fathers
243,082
243,082
(0.020)
(0.361)
-4.129
-0.052***
-1.02***
Logit Marginal eect
p̄ = 0.265
563,754
-2.443
(0.296)
-0.646***
Logit
Sons of women born 45-55
-0.159
LP
563,754
563,754
(0.015)
(0.219)
-2.456
-0.033**
LP
p = 0.247
Logit Marginal eect
p = 0.265
Logit
-0.650***
LP
Sons of men born 45-55 with low educated fathers
Notes: The top panel shows the estimated eects of father's reform status Reform_father on sons crime, and the bottom panel shows the estimated eects of mother's reform status
Reform_mother on son's crime. 26.45% of men in the considered sample have been convicted at least once. Of those men who have a low educated grandfather on their father's side 24.68%
have been convicted. Of those men who have a low educatedp
grandfather on their mother's side 27.78% have been convicted. Columns (1) and (4) present linear probability model (LP)
estimation with weighted least squares. The used weights are: x0 b(1 − x0 b), which are obtained from a rst stage OLS estimation. The level of observation are sons born before 1994, whose
parents were born between 1945-1955. Column (2) and (5) show the results of a logit estimation; we report the coecient of the log odds ratio coecient. The marginal eect for the logit
model are presented in columns (3) and (6). We reduced the sample on father's or mother's birth municipality and birth cohort level to estimate the logit model. Reducing the sample to the
following parent's cohort-birth municipality cells: for sons with a father born 1945-1955 to 10,607 cells, for sons whose father has a low educated father to 10,247 cells, for sons whose mother
was born 1945-1955 to 10,647 cells, and whose mother had a low educated father to 10,324 cells. Robust Standard errors in parentheses, clustered by municipality of birth. *** p<0.01, **
p<0.05, * p<0.1. All regressions include a full set of birth municipality dummies and birth cohort dummies of father's or mother's.
Observations
Change share convicted
Reform_mother
Specication
Sample
Panel B: Mother's reform assignment
Observations
Change share convicted
Reform_father
Specication
Sample
Panel A: Father's reform assignment
with low educated grandfathers separately. Sons of men and women born in Sweden between 1945 and 1955.
the probability of their sons having ever been convicted. Marginal eects are scaled by 100. Estimations for the entire sample and for sons
Table 8: Linear Probability Model estimates and Logit Model estimates of the marginal eects of father's and mother's reform assignment on
Table 9: Linear Probability Model estimates of the eects of father's and mother's reform
assignment on the probability of their sons having ever been convicted to a prison sentence.
Marginal eects are scaled by 100. Estimations for the entire sample and for sons with low
educated grandfathers separately. Sons of men and women born in Sweden between 1945
and 1955.
Sample
Probability prison
All Sons
Sons with low educated grandfather
p = 0.034
p = 0.028
0.015
0.005
(0.093)
(0.123)
0.448
0.169
563,754
243,082
All Sons
p = 0.034
p = 0.028
Reform_father
Change share prison
Observations
Sample
Probability prison
Reform_mother
0.038
0.004
(0.093)
(0.130)
1.093
0.108
595,138
255,075
change share prison
Observations
Notes: The reported estimates are based on a weighted least squares estimation. 3.43% of all men
in the sample have been to prison. 2.80% of men who have a low educated grandfather on their
father's side have been convicted to prison. 3.45% of men who have a low educated grandfather on
their mother's side have been convicted to prison. All regressions include either a full set of birth
municipality dummies of father or mother, and birth cohort dummies of father or mother. Robust
Standard errors in parentheses, clustered by municipality of birth of mother or father. *** p<0.01, **
p<0.05, * p<0.1.
A possible mechanism for the inverse eect of the reform on the criminal behavior
of the child generation is through fertility and family formation. An implication of
both the well known quality-quantity model and the time allocation model is that
increased educational attainments and earnings prospects will decrease the demand
for having children.
If there is a trade o between the number of children and
parental investments in each child, it may make up an inverse relation between
33
Table 10: Marginal Eects of negative binomial estimation of father's or mother's reform
assignment on the number of crimes sons are convicted of between 1981-2008. Estimations
for the entire sample and for sons with low educated grandfathers separately. Sons of men
and women born in Sweden between 1945 and 1955.
Sample
All Sons
1.245
1.210
-0.065**
-0.040
(0.031)
(0.050)
563,754
243,082
All Sons
Average number convicted crimes
1.538
1.442
Reform_mother
0.010
0.069
(0.046)
(0.055)
595,138
255,075
Average number convicted crimes
Reform_father
Observations
Sample
Observations
Notes: Marginal eects are reported, ȳ is the average number of convicted crimes in the sample.
Experiment_father or Experiment_mother is a dummy indicating the reform status of the father
or mother of the son. Robust Standard errors in parentheses, clustered by municipality of birth of
the son's father (Panel A) or mother (Panel B). *** p<0.01, ** p<0.05, * p<0.1. All regressions
include a full set of birth municipality dummies and birth cohort dummies of father (Panel A)
or mother (Panel B). The estimation method is a negative binomial model to relax the Poisson
model assumption of equal conditional mean and conditional variance of the outcome variable, but
maintaining an exponential mean.
being assigned to the reform and the inverse relation to child criminality.
To further investigate this possible mechanism we estimate the eect of the
reform on fertility behavior.
We use four dierent outcomes:
the total number
of children, the probability of having children, the age at birth of rst child and,
nally, the probability of having children while still a teenager. Since we only nd
signicant eects of the reform on sons to fathers aected by the reform, we restrict
the sample to men.
15
As can be seen in the results of these estimates shown in table
11, the only signicant eect is on the probability of having a child as a teenager.
15 The
results for women are qualitatively the same, the only dierence is that women who were
assigned to the reform are less likely to ever have a child on a 10 percent signicance level.
34
However, since only 1.3 percent of the population have children as teenagers and,
more importantly, the reform only decreased this probability by between 0.2 and 0.3
percent, we conclude that we cannot attribute the eect of the reform to changes
in fertility behavior. The results on all other fertility outcome variables shown are
estimated to be zero.
Table 11: Estimation of the eects of the reform on the probability of ever having
a child, the number of children, the age at birth of rst child and the probability of
teenage paternity. Marginal eects are scaled by 100. Estimations by birth cohorts
for the entire sample and for men with low educated fathers separately. Men born in
Sweden between 1945 and 1955.
Dependent variable
Specication
ever child
number children
age birth rst child
teenage
LP
Poisson
Neg binomial
LP
0.813
1.896
Sample:
Average dep var
Reform
Observations
Men born 45-55
Reform
Observations
0.017
-0.093
-0.004
0.106
-0.263**
(0.185)
(0.007)
(0.075)
(0.106)
622,583
622,583
505,679
622,583
Sample:
Average dep var
27.054
0.822
1.912
26.524
0.019
-0.096
0.001
0.064
-0.210**
(0.273)
(0.010)
(0.048)
(0.100)
264,679
264,679
217,517
264,679
Notes: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Stan-
dard errors are clustered by municipality level. All estimations include a set of
municipality dummies and cohort dummies.
The level of observation are men
born between 1945 and 1955, all men born between 1945-1955. Experiment is a
indicator variable for reform assignment.
6.3 The common trends assumption
As is well known, an identifying assumption underlying the dierences-in-dierences
estimator is that any trend in the outcome variable is common in the treatment and
comparison groups over the period of comparison.
35
This assumption is untestable
because it relates to the counterfactual change in the treatment group. However,
an indication can be obtained by testing whether the trends are common in the two
sets of groups before the reform and indeed after the reform as well.
In our sample we have 12 groups of municipalities indexed by which cohort was
rst assigned to the reform. To implement the common trends test we used only the
municipalities that rst implemented the reform for the 1947 cohort onwards (i.e.
10 of the 12 set of municipalities). We compared the trend of criminal behavior of
individuals across these municipalities for all cohorts that were not aected by the
reform. The pooled regression of these groups is
where
y = α + βt + γ 0 m + δ 0 t ∗ m + ν ,
m is a set of dummies indicating the group to which the municipality belongs
based on the cohort for which it rst implemented the reform;
that represents the cohorts 1945-1955. A joint test of
F
statistic of
F (9;7,090)=1.15
with
P =0.323,
t
is a linear trend
δ1 = δ2 = ... = δ10 = 0
gives a
with 7,090 cohort-municipality cells
before treatment. This implies the hypothesis of common trends in crime for the
pre-treatment cohorts for all groups of municipalities cannot be rejected.
For post-treatment trends in crime we only compare crime between the municipalities that implemented the reform for cohorts born before 1954. We compare the
criminal behavior of individuals across these municipalities for the cohorts aected
by the reform. A joint test of equality of the coecients on the interaction term of
the above pooled model yields F(9, 4,808) with P=0.1303, where 4,808 is the number of cohort-municipality cells that are treated. This means that the hypothesis of
a common trend in criminal behavior for the treated cohorts is the same across the
groups of municipalities that implemented the reform for dierent cohorts cannot
be rejected.
Both these tests are strong evidence in favor of the key identifying
assumption for our dierence-in-dierences approach to the problem.
36
7 Conclusion
We nd a very strong relation between own educational attainments and criminal
behavior in our data: every year of additional education is associated with a decrease
of about 7.5 percent in the probability of being convicted for a crime and a 15
percent decrease in the probability of a prison sentence.
We also nd a strong
relation between parental education and crime. On average, one year of additional
education of the father is associated with an about 2.1 percent decrease in the
probability of being convicted and an about 3.5 percent decrease in the probability
of a prison sentence. To what extent do these strong correlations give guidance to
the likely eects of a policy intervention designed to improve educational attainment
of the least skilled? The staggered implementation of a major Swedish education
reform in the 1950s and early 60s, which among other things increased compulsory
schooling, provides an ideal opportunity to answer this question.
Using administrative data that compares children of the same cohorts, but educated under dierent systems, we nd strong negative and signicant eects of the
reform, particularly when we focus on cohorts whose criminal activity is recorded
at a younger age.
Thus, for the youngest cohorts, born between 1954 and 1955,
the point estimate suggests a 1.3 percentage points, corresponding to 5 percent, decrease in the probability of being convicted from being assigned to the post reform
school system. In the group from homes with low educated fathers the eect seems
to be somewhat larger, which is consistent with a larger eect on both educational
attainments and labor earnings in this group found in Meghir and Palme (2005).
The striking result of this paper however is the eect of the reform on the children
of those originally aected: there is a signicant eect of paternal assignment to the
reform on the probability of being convicted corresponding to an average reduction of
37
about 2.5 percent. However, we found no signicant eect from mother's assignment
to the reform. The intergenerational eect of education on criminal behavior can
operate through several dierent channels. The result that the reform had a stronger
eect on criminal behavior in the child generation - together with the result obtained
in Holmlund, Lindhal, and Plug (forthcoming) that the reform had a weak, although
signicant, eect on the educational attainments in the next generation - suggests
that eect did not operate solely through increased education in the child generation.
Another conceivable channel is through changes in fertility induced by the reform.
Our data allowed us to estimate reform eects on four dierent outcomes related to
fertility behavior. Since our results suggest that the eect operated through father's
reform assignment, we restricted the sample to men. The only signicant eect was
a decreased probability of having a child as teenager. However, the eect was far
too small to serve as a major explanation of the decreased criminality in the next
generation.
Given the data at our disposal, there are several channels that we are not able to
distinguish between. The most obvious one is that improved earnings possibilities
provide the parents with improved economic resources to invest in the upbringing of
their children. However, since crime is also a manifestation of a basic lack of social
skills or other emotional decits it is also conceivable that the eect of parental
education works through other channels than solely economic resources. A possible
interpretation of the fact that we nd signicant eects of the father's rather than
the mother's reform assignment on the son's criminal behavior is consistent with
the notion that these alternative explanations may be important, in view of the fact
that female labor force participation is very high in Sweden.
38
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The Journal
8 APPENDIX
8.1 Reform Appendix
Table 12: Quantitative development of the comprehensive school experiment 1949
to 1962.
Year
Municipalities
Cumulative Number
Percentage share
Number of
Number of
classes
students
1949/50
14
1.3
172
2 483
1950/51
20
1.9
379
7 529
1951/52
25
2.4
682
14 635
1952/53
30
2.9
1 009
22 725
1953/54
37
3.5
1 525
35 784
1954/55
46
4.4
2 516
61 498
1955/56
59
5.6
3 394
84 941
1956/57
71
6.7
4 393
109 694
1957/58
96
9.1
5 702
143 370
1958/59
142
13.5
8 036
196 343
1959/60
217
20.6
11 191
266 042
1960/61
295
28.0
14 283
333 094
1961/62
415
39.4
18 665
436 595
Note: The 1952 division of municipalities (total: 1 052). Source: Marklund
43
8.2 Data appendix
Table 13: The Sample
Number observations
All
Male
Female
1,340,857
685,056
655,801
Parent generation:
Cohorts 1945-1955
Father's education available
881,742
452,433
429,309
Of which low educated father
560,273
287,396
272,877
63.54
63.52
63.56
1,621,758
833,564
788,194
Paternal grandfather's education available
802,451
412,619
389,832
Of which with low educated grandfather
511,980
263,319
248,661
63.80
63.82
63.79
Maternal grandfather's education available
836,632
430,357
406,275
Of which with low educated grandfather
538,228
276,779
261,449
64.33
64.31
64.35
Percent
Children generation:
All children of parent generation
Percent
Percent
Notes: We only present the number of observations that are available on father's and
grandfather's education level, because we will condition on father's or grandfather's
education level in the analysis. We only have information on the highest level of
education for those individuals that are not older than 60 years in the year of the 1970
census. We report the number of individuals in each sample, the number of individuals
for whome we have information on the highest level of education on their fathers or
grandfathers and the share of those for which we have this information with the lowest
education level. For the children generation with low educated grandfathers on their
father's side of the family, we consider those children whose father was born between
1945 and 1955. For the children generation with low educated grandfathers on the
mother's side of the family we consider those whose mother was born between 1945
and 1955.
44
Table 14: Number of all convictions in Sweden between 1981-2008
Number of convicted persons
Number of persons convicted for prison
All
Male
Female
1,249,569
966,790
282,779
366,639
344,919
21,720
Number of convictions in total
3,014,811
2,534,337
480,474
Number of prison sentences in total
1,204,711
1,115,428
89,283
age 15 -24
1,128,125
950,413
177,712
age 25-34
710,177
605,445
104,732
age 35-44
577,693
483,821
93,872
age 45-54
355,396
296,971
58,425
age 55-64
161,367
133,788
27,579
age 65-80
76,296
59,138
17,158
>
5,757
4,761
996
Convictions by age group
age
80
78% to 85% of the convictions are males.
Table 15: Data on all suspected crimes in Sweden between 1991-2009. Sons
of men or women born 1945-1955.
Number of persons suspected for a crime 1991-2009
All crimes
Excluding trac
Excluding some trac
129,683
117,279
124,487
20.95
18.94
20.11
Sample: Sons of men born 45-55
Percent of sample
Sample: Sons of men born 45-55 with low educated father
Percent of sample
54,542
48,888
52,222
20.71
18.57
19.83
133,953
120,748
129,217
20.50
18.48
19.78
Sample: Sons of women born 45-55
Percent of sample
Sample: Sons of women born 45-55 with low educated father
Percent of sample
55,210
49,294
129,217
19.95
17.81
19.78
Notes: The category excluding all trac crimes excludes the crime categories listed in table 17. The category excluding some trac crimes excludes the categories "Driving without a license", "Allowed driving without license" and "Override provision".
45
Figure 2: Life cycle convicted crimes
Figure 3: Life cycle convicted crimes
46
Table 16: Persons found guilty of criminal oences, by principal oence
Number of
Crimes against penal code
Of which
Crimes against life and health
Share, %
convictions, 2009
59,542
42.1
9,744
6.9
150
0.1
Of which
Murder and man-slaughter
Assault, gross assault
9,268
Sexual oences
1,090
6.5
0.8
Of which:
Rape
Theft, robbery, other oences of stealing
256
29,393
0.2
20.8
Of which:
Theft, gross theft
Petty theft
Robbery, gross robbery
Vehicle theft
Fraud and other dishonesty
Crimes inicting damage
Violent threat to public servant
Other
9,233
6.5
17,953
12.7
1,049
0.7
824
0.6
3,175
3,316
2,544
10,280
Crimes to other penal legislation
Crimes against the Road trac oences act
2.2
2.3
1.8
7.3
82,035
57.9
13,253
9.4
47,020
33.2
Of which
Drunken driving, gross drunken driving
Crimes against the Narcotics drugs act
Crimes against the Act on smuggling
Other
All crimes
18,525
2,076
14,414
141,577
13.1
1.5
10.2
100
Persons found guilty of criminal oences, by principal oence, 2009. Source:
Kriminalstatistik, Rättsstatistisk årsbok, Statistisk årsbok, Statistiska Meddelanden (R 11 SM).
47
48
0.01
0.05
0.05
0.06
0.17
0.2
0.57
1.31
1.45
2.81
7.25
24.43
61.64
Notes: Criminal oences related to trac violations persons were suspected for. Register data on all crimes individuals have been suspected for between
January 1991 and June 2009 in Sweden.
83
1,201
Involuntary manslaughter in connection with accident
Other crimes against maritime law, imprisonment in the range of penalties
1,393
Causing bodily injury or disease related to trac accident
314
3,896
Cause of danger for another in connection with trac or in trac
Maritime Act: Drunkenness
9,018
Gross negligence in trac
321
9,941
Allowed driving without a license
Other trac oense, imprisonment in range of penalties
19,346
Hit and run
414
49,828
Driving or aggravated driving under the inuence of drugs alone
Override provision
167,958
423,809
4,073,985
687,522 (16.876%)
Number of cases Percentage of trac crimes
or under the inuence of both alcohol and drugs
Drink-driving or aggravated drink-driving under the inuence of alcohol alone
Driving or aggravated driving without a license
Total number of crimes
Total number of trac crimes
Description of trac violation
Table 17: Trac crimes persons were suspected for, January 1991 - June 2009.
Table 18: Linear probability estimates of the association of own education
levels and criminal behavior. Men born between 1945-1955.
Dependent variables
Probability prison
p̄ = 0.2531
p̄ = 0.0538
Education Levels
Vocational
Upper secondary
Upper secondary +
College/University
PhD
Observations
≥
1 year
-0.161
0.031
(1.190)
(0.273)
-7.471***
-3.928***
(1.028)
(0.287)
-10.549***
-5.113***
(0.913)
(0.288)
-13.782***
-5.929***
(0.923)
(0.395)
-19.759***
-7.183***
(0.713)
(0.545)
684,625
684,625
Notes: Results are scaled by 100. Level of observation are men born between 1945
and 1955 in Sweden. Robust standard errors in parentheses, clustered by birth municipality. Signicance levels *** p<0.01, ** p<0.05, * p<0.1. All regressions include
a full set of birth cohort dummies and birth municipality dummies.
49
Table 19: Linear probability model estimates of the association of parental education levels and own criminal behavior. Sons of parents born 1945-1955.
Dependent variables
Probability prison
p̄ = 0.2645
p̄ = 0.0343
-2.075***
-0.694***
(0.214)
(0.088)
-8.083***
-2.342***
(0.339)
(0.142)
-9.719***
-2.457***
Panel A: Education levels father
Vocational
Upper secondary
Upper secondary +
≥
1 year
College/University
PhD
Observations
(0.388)
(0.135)
-12.535***
-2.900***
(0.463)
(0.214)
-13.829***
-3.029***
(0.551)
(0.303)
754,121
754,121
-4.356***
-1.556***
(0.291)
(0.102)
-8.119***
-2.648***
Panel B: Education levels mother
Vocational
Upper secondary
Upper secondary +
≥
1 year
College/University
PhD
Observations
(0.473)
(0.181)
-10.015***
-2.854***
(0.381)
(0.173)
-12.324***
-3.242***
(0.508)
(0.230)
-14.553***
-3.443***
(1.059)
(0.390)
754,121
754,121
Notes: Level indicates a dummy for each education level. The omitted education level is
the lowest education level combined levels 1 and 2. Level of observation are sons of parents
born between 1945 and 1955 in Sweden. Robust standard errors in parentheses, clustered
by birth municipality. Signicance levels *** p<0.01, ** p<0.05, * p<0.1. All regressions
include a full set of birth cohort dummies and birth municipality dummies.
50

The E ect of Education Policy on Crime: An Intergenerational

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